Mapping spatial colleague connectivity patterns from individual-level registry data to inform regional pandemic interventions.

A concern in infectious disease modelling is how accurately population mixing is incorporated, as it shapes the type and frequency of contacts through which infection spreads, and consequently, estimated intervention effectiveness. Although synthesizing mixing patterns from diary-based surveys is an established framework, geographical information is poorly or sparsely captured. Here we propose a generalizable workflow to quantify geographical connectivity from job registry data covering over 8 million Dutch working population. The derived colleague connectedness shows heterogeneous spatial patterns, quantified from the number of connections per municipality triplet, two residential municipalities and one shared workplace municipality. We illustrate the epidemiological relevance of this spatial connectivity by using SARS-CoV-2 Omicron as an example: a two-fold increase in within-province connections was associated with a 3.7-day earlier (95% CI: 0.6 to 6.6 days) Omicron onset, and between-province connectivity was associated with a 2.5 days earlier (95% CI: -1.0 to 6.2 days) onset. Based on our estimates of spatial connectivity, we quantified the number of colleague connections that would be removed in case of regional mobility restrictions such as a lockdown: locking down the whole province Zeeland would remove 2.6% of colleague links at the national level while the city Amsterdam alone would remove 10.0%. In future modelling studies, these highly fine-grained spatial connectivity data could be used as spatial mixing matrices to more explicitly capture the connectedness and dependency between regions to inform more tailored policy measures.